unsloth-tokenizer

What problem does it solve?

This Skill helps you compare, analyze, and optimize tokenizers used with Unsloth models to improve efficiency and accuracy across NLP tasks.

Core Features & Use Cases

• Tokenizer comparison: Benchmark multiple tokenizers to identify the most token-efficient option for your workload.
• Tokenization analysis: Inspect token counts, vocabulary coverage, and term handling for domain texts.
• Seamless integration: Swap tokenizers in your Unsloth pipeline with minimal code changes.

Quick Start

Use the skill to compare two tokenizers and surface performance metrics for your production prompts, then select the best candidate for deployment. To run locally, install the required Python packages and execute the provided example scripts. Compare Two Tokenizers: from unsloth.tokenizer import compare_tokenizers results = compare_tokenizers( text="The quick brown fox jumps over the lazy dog", tokenizer1="meta-llama/Llama-3.2-1B", tokenizer2="gpt2" ) print(f"Llama-3.2: {results['tokenizer1']['tokens']} tokens") print(f"GPT-2: {results['tokenizer2']['tokens']} tokens") print(f"Difference: {results['reduction']}")